{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集）进行分类器训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、数据加载\n",
    "\n",
    "### 1.1 读取数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n",
      "None\n",
      "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
      "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
      "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
      "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
      "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
      "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
      "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
      "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
      "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
      "\n",
      "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
      "count  768.000000                768.000000  768.000000  768.000000  \n",
      "mean    31.992578                  0.471876   33.240885    0.348958  \n",
      "std      7.884160                  0.331329   11.760232    0.476951  \n",
      "min      0.000000                  0.078000   21.000000    0.000000  \n",
      "25%     27.300000                  0.243750   24.000000    0.000000  \n",
      "50%     32.000000                  0.372500   29.000000    0.000000  \n",
      "75%     36.600000                  0.626250   41.000000    1.000000  \n",
      "max     67.100000                  2.420000   81.000000    1.000000  \n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "print(data.info())\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 数据集基本信息"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "共有768个样本，8个特征，1个标签，没有缺失数据，数据类型都为数值型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、数据可视化\n",
    "### 2.1 标签类别可视化\n",
    "2个类别的数据数量上存在差异, 数量比约为2:1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x193b1cc5080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.Outcome)\n",
    "plt.xlabel('outcome')\n",
    "plt.ylabel('numner of outcome')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzAAAAKGCAYAAACV7H7HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkkbDAkaSJEnSaFjASJIkSRqN/wM42/trnZhCkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x193b1a39be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datadis = data\n",
    "\n",
    "datadis_corr = datadis.corr().abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(datadis_corr,annot=True)\n",
    "# Mask unimportant features\n",
    "sns.heatmap(datadis_corr, mask=datadis_corr < 1, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从热图中看到，怀孕次数与是否糖尿病有相关性，这也符合医学的相关解释，因为怀孕中容易患妊娠期糖尿病，如果怀孕次数多的话，就比较容易得糖尿病。其次是口服葡萄糖耐受试验中，2小时的血浆葡萄糖浓度和体重、年龄。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 数据特征变换\n",
    "\n",
    "### 3.1 Age字段先聚类，然后在进行One-hot编码处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0            6      148             72             35        0  33.6   \n",
      "1            1       85             66             29        0  26.6   \n",
      "2            8      183             64              0        0  23.3   \n",
      "\n",
      "   DiabetesPedigreeFunction  age__0  age__1  age__2  \n",
      "0                     0.627       0       0       1  \n",
      "1                     0.351       0       1       0  \n",
      "2                     0.672       0       1       0  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "ks = KMeans(n_clusters=3,random_state=0)\n",
    "age_cluster = data.loc[:,['Age']]\n",
    "ks.fit(age_cluster)\n",
    "clu = ks.predict(age_cluster)\n",
    "\n",
    "data['age_clu'] = clu\n",
    "# print(pd.get_dummies(data['age_clu'],prefix = 'age_'))\n",
    "\n",
    "data = data.join(pd.get_dummies(data['age_clu'],prefix = 'age_'))\n",
    "\n",
    "train_label = data[\"Outcome\"]\n",
    "train_data = data.drop([\"Outcome\",\"Age\",\"age_clu\"],axis = 1)\n",
    "\n",
    "print(train_data.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 拆分数据集 测试集占20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(train_data,train_label,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 对血压等数值型数据进行标准化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pregnancies  age__0  age__1  age__2  Glucose_std  BloodPressure_std  \\\n",
      "661            1       0       1       0     2.457359           0.346743   \n",
      "122            2       0       1       0    -0.437196           0.244363   \n",
      "113            4       0       1       0    -1.412536          -0.369921   \n",
      "14             5       0       0       1     1.419095           0.141982   \n",
      "529            0       0       1       0    -0.311346          -0.216350   \n",
      "\n",
      "     SkinThickness_std  Insulin_std   BMI_std  DiabetesPedigreeFunction_std  \n",
      "661           1.395074    -0.699657  1.352245                      2.785944  \n",
      "122           0.584572     0.152162  0.176195                     -0.187638  \n",
      "113          -1.285816    -0.699657  0.226778                     -0.226685  \n",
      "14           -0.101237     0.791026 -0.810169                      0.362024  \n",
      "529          -1.285816    -0.699657 -0.961917                      0.581288  \n"
     ]
    }
   ],
   "source": [
    "#标准化处理\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "std = StandardScaler()\n",
    "\n",
    "# x_train = std.fit_transform(x_train)\n",
    "# x_test = std.transform(x_test)\n",
    "\n",
    "\n",
    "std_col = x_train.columns[1:7].tolist()\n",
    "\n",
    "std_train = x_train[std_col].values\n",
    "std_test = x_test[std_col].values\n",
    "\n",
    "std_train = std.fit_transform(std_train)\n",
    "std_test = std.transform(std_test)\n",
    "\n",
    "std_train_df = pd.DataFrame(std_train,columns=['Glucose_std','BloodPressure_std','SkinThickness_std','Insulin_std','BMI_std','DiabetesPedigreeFunction_std'],index = x_train.index)\n",
    "std_test_df = pd.DataFrame(std_test,columns=['Glucose_std','BloodPressure_std','SkinThickness_std','Insulin_std','BMI_std','DiabetesPedigreeFunction_std'],index = x_test.index)\n",
    "\n",
    "x_train_new = pd.concat((x_train,std_train_df),axis = 1)\n",
    "x_train_new.drop([\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\"], axis = 1,inplace=True)\n",
    "x_train = x_train_new\n",
    "\n",
    "x_test_new = pd.concat((x_test,std_test_df),axis = 1)\n",
    "x_test_new.drop([\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"DiabetesPedigreeFunction\"], axis = 1,inplace=True)\n",
    "x_test = x_test_new\n",
    "print(x_test.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4、模型训练与优化参数\n",
    "### 4.1 Logistic回归，并选择最佳的 正则函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#用GridSearchCV来确定最优的超参数， C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和 正则函数penalty（L2/L1）\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import f1_score,precision_score,recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳得分 -0.49439388309\n",
      "最优参数 {'C': 1, 'penalty': 'l2'}\n",
      "测试集上的模型得分 -0.429553601369\n",
      "f1 score is 0.674\n",
      "precision score is 0.744\n",
      "Classification report for classifier GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring='neg_log_loss', verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.91      0.87       107\n",
      "          1       0.74      0.62      0.67        47\n",
      "\n",
      "avg / total       0.81      0.82      0.81       154\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "penaltys = ['l1','l2']\n",
    "c_logistic = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = c_logistic)\n",
    "\n",
    "grid_search = GridSearchCV(LogisticRegression(solver='liblinear', multi_class='ovr'),tuned_parameters,cv=5,scoring=\"neg_log_loss\")\n",
    "grid_search.fit(x_train,y_train)\n",
    "\n",
    "print('最佳得分',grid_search.best_score_)\n",
    "print('最优参数',grid_search.best_params_)\n",
    "\n",
    "print('测试集上的模型得分',grid_search.score(x_test,y_test))\n",
    "\n",
    "y_pred = grid_search.predict(x_test)\n",
    "print(\"f1 score is %1.3f\"%f1_score(y_test,y_pred))\n",
    "print(\"precision score is %1.3f\"%precision_score(y_test,y_pred))\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (grid_search, classification_report(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 线性 SVM，并选择最佳正则参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳得分 0.768729641694\n",
      "最优参数 {'C': 0.10000000000000001}\n",
      "测试集上的模型得分 0.818181818182\n",
      "f1 score is 0.674\n",
      "precision score is 0.744\n",
      "Classification report for classifier GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,\n",
      "     verbose=0),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'C': array([  1.00000e-03,   1.00000e-02,   1.00000e-01,   1.00000e+00,\n",
      "         1.00000e+01,   1.00000e+02,   1.00000e+03])},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring=None, verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.91      0.87       107\n",
      "          1       0.74      0.62      0.67        47\n",
      "\n",
      "avg / total       0.81      0.82      0.81       154\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#SVM 训练\n",
    "from sklearn.svm import LinearSVC\n",
    "svc_linear = LinearSVC(random_state=0)\n",
    "\n",
    "c_svc =  np.logspace(-3, 3, 7)\n",
    "tuned_parameters = dict( C = c_svc)\n",
    "grid_search_1 = GridSearchCV(svc_linear,tuned_parameters,cv=5)\n",
    "grid_search_1.fit(x_train,y_train)\n",
    "print('最佳得分',grid_search_1.best_score_)\n",
    "print('最优参数',grid_search_1.best_params_)\n",
    "\n",
    "print('测试集上的模型得分',grid_search_1.score(x_test,y_test))\n",
    "\n",
    "y_pred = grid_search_1.predict(x_test)\n",
    "print(\"f1 score is %1.3f\"%f1_score(y_test,y_pred))\n",
    "print(\"precision score is %1.3f\"%precision_score(y_test,y_pred))\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (grid_search_1, classification_report(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 RBF核的 SVM，并选择最佳的超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score is:  0.762214983713 best parameters are  {'C': 359.38136638046257, 'gamma': 0.001}\n",
      "f1 score is 0.643\n",
      "precision score is 0.730\n",
      "Classification report for classifier GridSearchCV(cv=5, error_score='raise',\n",
      "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
      "  tol=0.001, verbose=False),\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'gamma': [1e-06, 1e-05, 0.0001, 0.001, 0.01], 'C': array([  1.00000e-01,   2.78256e-01,   7.74264e-01,   2.15443e+00,\n",
      "         5.99484e+00,   1.66810e+01,   4.64159e+01,   1.29155e+02,\n",
      "         3.59381e+02,   1.00000e+03])},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
      "       scoring=None, verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.91      0.87       107\n",
      "          1       0.73      0.57      0.64        47\n",
      "\n",
      "avg / total       0.80      0.81      0.80       154\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#RBF核SVM正则参数调优\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.utils.fixes import signature\n",
    "from sklearn.svm import SVC\n",
    "#svc = SVC(kernel='rbf',class_weight={0:0.1,1:0.9}) #这里加大了糖尿病正样本的权重，个人觉得应高提高召回率，不漏过真正的病人。牺牲一些precision。\n",
    "svc = SVC(kernel='rbf',random_state=0)\n",
    "c = np.logspace(-1,3, 10)\n",
    "gamma_s = [  1.00000000e-06, 1.00000000e-05,   1.00000000e-04,   1.00000000e-03,   1.00000000e-02] #np.logspace(-5, -2, 4)\n",
    "\n",
    "tuned_parameters = dict(gamma = gamma_s, C = c)\n",
    "grid_search_2 = GridSearchCV(svc,tuned_parameters,cv=5,return_train_score=True)\n",
    "grid_search_2.fit(x_train,y_train)\n",
    "\n",
    "print('best score is: ',grid_search_2.best_score_, 'best parameters are ',grid_search_2.best_params_)\n",
    "\n",
    "y_pred = grid_search_2.predict(x_test)\n",
    "print(\"f1 score is %1.3f\"%f1_score(y_test,y_pred))\n",
    "print(\"precision score is %1.3f\"%precision_score(y_test,y_pred))\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (grid_search_2, classification_report(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 绘制 PR曲线 计算平均PR score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.68\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5aAwM7Os/wJoIgoPJviODAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x193b1e3a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_score = grid_search_2.decision_function(x_test)\n",
    "from sklearn.metrics import average_precision_score,classification_report\n",
    "average_precision = average_precision_score(y_test, y_score)\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "    average_precision))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_score)\n",
    "\n",
    "# In matplotlib < 1.5, plt.fill_between does not have a 'step' argument\n",
    "step_kwargs = ({'step': 'post'}\n",
    "               if 'step' in signature(plt.fill_between).parameters\n",
    "               else {})\n",
    "plt.step(recall, precision, color='b', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(\n",
    "          average_precision))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 plot CV误差曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x193b20bc588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_means = grid_search_2.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid_search_2.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid_search_2.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid_search_2.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(c)\n",
    "number_gamma_s = len(gamma_s)\n",
    "test_scores = np.array(test_means).reshape(n_Cs, number_gamma_s)\n",
    "train_scores = np.array(train_means).reshape(n_Cs, number_gamma_s)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs, number_gamma_s)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs, number_gamma_s)\n",
    "\n",
    "x_axis = np.log10(c)\n",
    "for i, value in enumerate(gamma_s):\n",
    "    plt.errorbar(x_axis, test_scores[:, i], yerr=test_stds[:, i], label=str(gamma_s[i]) + ' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:, i], yerr=train_stds[:, i], label=str(gamma_s[i]) + ' Train')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('nscores')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
